Hierarchical learning in polynomial Support Vector Machines
arXiv:cond-mat/0010423
Abstract
We study the typical properties of polynomial Support Vector Machines within a Statistical Mechanics approach that allows us to analyze the effect of different normalizations of the features. If the normalization is adecuately chosen, there is a hierarchical learning of features of increasing order as a function of the training set size.
22 pages, 7 figures, submitted to Machine Learning